Lossless compression techniques may effectively enhance the coding efficiency since it saves the bandwidth and generates a lossless re-constructed signal. However, due to the inherent entropy between signals, the compression efficiency for different signals varies significantly with different compression solutions, and there is a high requirement for complexity in real-time transmission. Therefore, it is generally hard to further realize the tradeoff between the coding efficiency and the complexity, as well as the adaptation for different signals.
The existing lossless compression technique is mainly applicable to audio storage so as to acquire a higher compression ratio. However, such application brings higher complexity. In another technique, every sample of the signal is compressed and encoded so as to acquire a larger compression ratio. However, when every sample of different input signals is compressed in the same compression mode, the signal characteristics are neglected and it is highly possible that the compression mode which is not suitable for the input signal is used to compress and encode the input signal. Thus, the compression efficiency is degraded severely. In a worse situation, the signal may not even be able to be compressed and encoded.